Polymorphisms in the apoptosis-associated genes FAS and FASL and risk of oral cancer and malignant potential of oral premalignant lesions in a Taiwanese population
Bibliographic record
Abstract
BACKGROUND: Our aim was to measure the relationship of FAS (-1377G>A and -670A>G), FASL (-844C>T) gene variants and risk of oral cancer. METHODS: Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) analysis was used to determine the FAS and FASL polymorphisms in 294 oral squamous cell carcinoma (OSCC), 53 oral submucous fibrosis (OSF), and 84 oral leukoplakia (OL) patients, as well as in 333 healthy controls. A standardized questionnaire was applied to collect demographic data, and potential confounding factors. JMP statistical software was used to analyze the association. RESULTS: FAS and FASL polymorphisms were not correlated with OSCC development or the malignant potential of OL by simple and multivariate logistic regression. However, a two- to fourfold difference in the risks of betel quid chewing, alcohol consumption, and smoking on OSCC development were observed between participants with different FAS polymorphisms. FAS polymorphisms were significantly correlated with the malignant potential of OSF. Multivariate logistic regression analysis indicated that FAS A(-1377)-G(-670) vs. G(-1377)-A(-670) haplotype (OR = 2.26, 95% CI = 1.16-4.41) was correlated with the malignant potential of OSF. CONCLUSIONS: We suggest that FAS and FASL polymorphisms are not significantly correlated with OSCC development or malignant potential of OL. The impact of substance usage on OSCC development could be differentiated by FAS polymorphisms. FAS A(-1377)-G(-670) haplotype may play a role in the malignant potential of OSF.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".